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https://doi.org/10.1007/s11739-020-02599-3 IM - ORIGINAL

Early emergency readmission frequency as an indicator of short‑, medium‑ and long‑term mortality post‑discharge from hospital

David Fluck1 · Paul Murray2 · Jonathan Robin3 · Christopher Henry Fry4 · Thang Sieu Han5,6

Received: 12 October 2020 / Accepted: 2 December 2020 / Published online: 26 December 2020

© The Author(s) 2020

Abstract

Frequent emergency readmissions, an indicator of quality of care, has been rising in England but the underlying reasons remain unclear. We examined the association of early readmissions with subsequent mortality in adults, taking into account the underlying presenting diagnoses and hospital length of stay (LOS). Data of alive-discharge episodes were prospectively collected between 01/04/2017 and 31/03/2019 in an National Health Service hospital, comprising 32,270 patients (46.1%

men) aged 18–107 years (mean = 64.0, ± SD = 20.5 years). The associations of readmission frequency within 28 days of discharge and mortality within 30 days and 6 months of hospital discharge, and over a 2-year period were evaluated, adjusted for presenting diagnoses, LOS, age and sex during the first admission. Analysis of all patients 18–107 years (reference: no readmission) showed mortality within 30 days was increased for 1 readmission: event rate = 9.2%, odds ratio (OR) = 3.4 (95% confidence interval (CI) = 2.9–4.0), and ≥ 2 readmissions: event rate = 10.0%, OR = 2.6 (95%CI = 2.0–3.3), and within 6 months for 1 readmission: event rate = 19.6%, OR = 3.0 (95%CI = 2.7–3.4), and ≥ 2 readmissions: event rate = 27.4%, OR = 3.4 (95%CI = 2.9–4.0), and over a 2-year period for 1 readmission: event rate = 25.5%, hazard ratio = 2.2 (95%CI = 2.0–

2.4), and ≥ 2 readmissions: event rate = 36.1%, hazard ratio = 2.5 (95%CI = 2.2–2.8). Within the age groups 18–49, 50–59, 60–69, 70–79 and ≥ 80 years, readmissions were also associated with increased risk of mortality within 3 months and 6 months of discharge, and over 2-year period. In conclusion, early hospital readmission predicts short-, medium- and long- term mortality post-discharge from hospital in adults aged 18–107 years, independent of underlying presenting conditions, LOS, age and sex. Further research focussing on safe discharge and follow-up patient care may help reduce preventable readmissions and post-discharge mortality.

Keywords Health economics · Healthcare services · Readmission prevention · Quality of care Abbreviations

CI Confidence interval

ICD International classification of diseases LOS Length of stay

NHS National Health Service OR Odds ratio

HR Hazard ratio

Introduction

Emergency readmission frequency has been a major focus of research of healthcare services, used as a measure of quality of care and cost-efficiency, primarily in older adults [1–4]. A study of over 14 million emergency admissions in US adults ≥ 18 years revealed that the cost of a readmis- sion within 30 days of discharge from hospital was about US $10,000 for sepsis and US $8500–9500 for respiratory

* Thang Sieu Han thang.han@rhul.ac.uk

1 Department of Cardiology, Ashford and St Peter’s Hospitals NHS Foundation Trust, Guildford Road, Chertsey, Surrey KT16 0PZ, UK

2 Department of Respiratory Medicine, Ashford and St Peter’s Hospitals NHS Foundation Trust, Guildford Road, Chertsey, Surrey KT16 0PZ, UK

3 Acute Medical Unit, Ashford and St Peter’s Hospitals NHS Foundation Trust, Guildford Road, Chertsey, Surrey KT16 0PZ, UK

4 School of Physiology, Pharmacology and Neuroscience, University of Bristol, Bristol BS8 1TD, UK

5 Department of Endocrinology, Ashford and St Peter’s Hospitals NHS Foundation Trust, Guildford Road, Chertsey, Surrey KT16 0PZ, UK

6 Institute of Cardiovascular Research, Royal Holloway, University of London, Egham, Surrey TW20 0EX, UK

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and cardiac conditions [2]. Another US study showed the cost of an early readmission after a discharge for coronary artery bypass graft was greater—$13,500 [4]. Early emer- gency readmissions are continuously recorded by National Health Service (NHS) hospitals. Healthwatch England ana- lysed data from 70 out of 125 hospitals and found that in 2017–18, the number of emergency readmissions increased by 9%; the fastest rate over the previous 5 years. There were 484,609 emergency readmissions to hospital within 30 days, a 22% rise over the previous 5 years, with numbers readmit- ted within 24 h of discharge (‘failed discharge’) from hos- pital rose by 33% in the same period [5, 6]. The underlying reasons for these increases in readmissions remain unclear and has led Healthwatch England to conclude that “Most troubling is that the sector still cannot report on how many emergency readmissions were unavoidable and which ones could be prevented, or use this insight to learn” [5]. How- ever, increased readmission could be explained, in part, by the rising admissions in recent years. The total annual number of hospital admissions across all English hospitals in 2006 was 11 million, which rose by 28% to over 14 mil- lion in 2016. The corresponding figures for those older than 65 years were 4 million in 2006 and 6 million in 2016, a rise of 46%, [7] while the trends in hospital length of stay (LOS) for those staying at least one night showed a marginal increase between 2010 and 2014, from 6.85 to 6.92 days [8].

Other than being an indicator of quality of care, early emergency readmission also reflects poor health status of an individual [9–11]. Hitherto, there is a paucity of data on its relationship with health consequences, such as mortality. In this study, we examined the associations of early readmis- sion frequency on all-cause mortality within 30 days and 6 months after discharge from hospital and over a 2-year period, in adults aged 18–107 years, taking into account the presenting diagnoses and LOS.

Methods

Participants and setting

In line with the NHS, information for every patient attending emergency department is recorded using Patient Adminis- tration System (PAS) including demographics, diagnosis, co-morbidities, dates of admission and discharge. Data of consecutive alive-discharge episodes over 2 years between 1 April 2017 and 31 March 2019 extracted from PAS in a single NHS hospital [12, 13].

Outcome measures

Information on the frequency of unplanned admissions and all-cause readmissions within 28 days, and all-cause

mortality within 30 days and 6 months after hospital dis- charge, and over 2-year period was recorded. Primary diag- noses presented in the first admission were coded according to international classification of diseases (ICD-10) [14].

Cancer and obstetrics spells were excluded in line with the NHS data collection for general hospital admissions [15].

LOS was calculated as the duration between the dates of first admission and first discharge.

Categorisation of variables

Age was categorised by decades from 50 years old: 50–59, 60–69 and 70–79 years. All those aged 18–49 years were grouped together due to low mortality rates, while those aged 80–107 years were combined together due to small numbers. The frequency of readmissions within 28 days of discharge was categorised into three groups: No readmis- sion, readmitted once, and readmitted ≥ 2 times. Prolonged LOS was considered as those staying in hospital ≥ 2 weeks (top 10th centile of LOS).

Statistical analysis

Continuous data are presented as mean ± standard deviation, except some skewed data sets that are shown as median val- ues (25%, 75% interquartiles). Chi-square tests were used to assess the relationship between readmission frequency and mortality, and Kruskal–Wallis H test to assess differ- ences in LOS between different categories of readmission frequency. The frequency of readmission was used to predict mortality within 30 days and within 6 months of hospital discharge using multivariable stepwise logistic regression, and mortality over a 2-year period using multivariable step- wise Cox regression. Kaplan–Meier curves were constructed to examine survival time after hospital discharge in relation to readmission frequency. Data were adjusted for age and sex and primary diagnosis at presentation and prolonged LOS during the first admission for all ages and for different age categories. Odds ratio (OR) and hazard ratio (HR) are given with 95% confidence intervals (CI). Analyses were performed using IBM SPSS Statistics, v25.0 (IBM Corp., Armonk, NY).

Results

Subject characteristics

A total of 14 878 men and 17 392 women of mean age 64 ± 20.5 years were studied. Table 1 shows patient charac- teristics including primary diagnoses presented in the first admission and age distribution. The proportions of patients with no readmission, one readmission and ≥ 2 readmissions

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within 28 days of first hospital discharge were 88.5, 8.1 and 3.3%, and over a 2-year period were 78.4, 13.5 and 8.1%, respectively. Readmission rates increased incrementally with age groups 18–49, 50–59, 60–69, 70–79 and ≥ 80 years

from 5.0, 5.8, 6.7, 8.0, to 13.7% for one readmission and 1.0, 1.5, 2.4, 3.3 to 6.7% for ≥ 2 readmissions, respectively.

There were 2.6, 6.8, and 10.2% of patients who died within 30 days, after 6 months and after 2 years post discharge, respectively. The mean age of death (81 ± 12 years) was similar among all groups.

Association of primary diagnoses and LOS during admission with readmission frequency Admissions for sepsis, diabetes, psychiatric, congestive heart failure, chronic obstructive pulmonary disease, pneumonia, urinary tract infection, musculoskeletal disorders and bodily pain, medical device-related complications, and prolonged LOS all linked to subsequent readmissions within 28 days of charge from hospital (Table 2). The median (interquartile range) of the preceding hospital LOS during the first hospital admission was 2.1 (0.9–5.1) days for no readmission, 6.6 (2.7–13.3) days for 1 readmission and 11.3 (6.7–19.8) days for ≥ 2 readmission (Kruskal–Wallis H test for group differ- ences: χ2 = 1765, p < 0.001).

Association of readmission frequency on mortality Among those who were not readmitted, readmitted once, and readmitted ≥ 2 times within 28 days of discharge, the mortal- ity rates within 30 days of discharge were 1.7, 9.2 and 10.0%

2 = 779, p < 0.001), and within 6 months of discharge were 4.8, 19.6 and 27.4% (χ2 = 1572, p < 0.001). Figure 1a–c shows that mortality rates increased progressively with age and increasing frequency of readmission. Within each age group of 18–49, 50–59, 60–69, 70–79 and ≥ 80 years, the mortality rates within 30 days of discharge for those who were not readmitted were 0.1, 0.5, 1.3, 2.1 and 4.1%; read- mitted once were 1.2, 2.4, 6.3, 8.9 and 14.2%; and readmit- ted ≥ 2 times were 1.1, 4.6, 7.1, 9.9 and 12.6% (Fig. 1a). The corresponding mortality rates for those who were readmitted within 6 months of first discharge were: 0.4, 1.5, 3.6, 5.7 and 11.9% for no readmission; 2.6, 7.6, 13.9, 19.8 and 29.5%

for one readmission; and 5.7, 10.8, 22.3, 32.0 and 31.7%

(Fig. 1b). Within 2 years of discharge, these were 0.6, 2.6, 5.5, 9.8 and 19.0% for no readmission, 3.1, 1.0, 18.0, 28.0 and 37.6% for one readmission, and 10.3, 13.8, 25.0, 41.9 and 42.4% (Fig. 1c).

The mortality rates within 30 days and 6 months of discharge, and over 2-year period all increased progres- sively with age, with higher rates for longer-term mortality (Fig. 2a–c). Analysis of short-term (within 30 days) mortal- ity showed that compared with those who were not read- mitted, patients who were readmitted just once or ≥ 2 times had similarly higher rates of mortality (Fig. 2a). When the analysis was extended to medium term (6 months) or long term (2-year period), mortality rates in those admitted once

Table 1 Characteristics of 14,878 men aged 18.0–104.1  years (mean = 63.9 ± SD = 19.3 years) and 17,392 women aged 18.0–106.7 (mean = 64.1 ± SD = 21.6 years)

a This group includes those who died within 30 days of admission

b Specific conditions within categories of disorders

n %

Age distribution

 18–49.9 years 8403 26.0

 50–59.9 years 4304 13.3

 60–69.9 years 4739 14.7

 70–79.9 years 6068 18.8

  ≥ 80 years 8756 27.1

Primary diagnosis on first admission

 Sepsis 1170 3.6

 Viral infections 145 0.4

 Haematological disorders 676 2.1

 Metabolic and endocrine disorders 1084 3.4

 Diabetesb 350 1.1

 Psychiatric disorders 279 0.9

 Neurological disorders 722 2.2

 Ophthalmic disorders 163 0.5

 Cardiovascular disorders 4416 13.7

 Congestive heart failureb 405 1.3

 Pulmonary disorders 3367 10.4

 Asthmab 207 0.6

 Chronic obstructive pulmonary diseaseb 470 1.5

 Pneumoniab 1692 5.2

 Gastrointestinal disorders 4144 12.8

 Dermatological disorders 1083 3.4

 Musculoskeletal disorders 3169 9.8

 Urological disorders 3686 11.4

 Urinary tract infectionb 1116 3.5

 Bodily pain 4344 13.5

 Bone fractures 2624 8.1

 Hip fracturesb 639 2.0

 Medical device-related complications 1015 3.1

 Others (not specified) 183 0.6

Stayed in hospital ≥ 2 weeks 3228 10.0

Number of readmissions within 28 days of discharge

 None 28,548 88.5

 Once 2666 8.3

  ≥ 2 times 1056 3.3

Mortality status

 Death within 30 days of first admission 834 2.6  Death within 6 months of first admissiona 2192 6.8

 Death within a 2-year period 3305 10.2

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and ≥ 2 times uncoupled from each other with the widest gap among those aged 60–79 years (Fig. 2b, c).

Compared with patients with no readmission, the OR of mortality within 30 days of discharge in those who were readmitted once was 3.4 (95%CI = 2.9–4.0) and ≥ 2 times was 2.6 (95%CI = 2.0–3.3) for all ages, and for each age category from 18–49, 50–59, 60–69, 70–79 and ≥ 80 year:

ORs were 10.7, 3.9, 4.0, 3.2 and 3.2 for 1 readmission and 10.2, 5.6, 3.7, 2.8 and 2.4 for ≥ 2 readmissions, respec- tively. The OR of mortality within 6 months of discharge in those who were readmitted once was 3.0 (95%CI = 2.7–3.4) and ≥ 2 times was 3.4 (95%CI = 2.9–4.0) for all ages, and for each age category from 18–49, 50–59, 60–69, 70–79 and ≥ 80 year: ORs were 7.5, 4.5, 3.3, 3.2 and 2.7 for 1 read- mission and 12.4, 5.9, 4.1, 5.1 and 2.7 for ≥ 2 readmissions, respectively (Table 3).

Compared to those with no readmission (mean for survival time from discharge = 30.2 months, 95%CI = 30.1–30.3), those who were readmitted once or ≥ 2 times had a sig- nificantly shorter survival with mean for survival time

from discharge = 24.9  days (95%CI = 24.4–25.4) and 21.9 (95%CI = 21.0–22.7) days, respectively, log-rank (Mantel–Cox) test: χ2 = 3876, p < 0.001 (Fig. 3). Mor- tality over the 2-year period was increased for those who were readmitted once: event rate = 25.5%, HR = 2.2 (95%CI = 2.0–2.4), and ≥ 2 times: event rate = 36.1%, HR = 2.5 (95%CI = 2.2–2.8) for all ages, and for each age category from 18–49, 50–59, 60–69, 70–79 and ≥ 80 year:

HRs were 4.9, 3.3, 2.7, 2.5 and 2.0 for 1 readmission and 12.4, 3.7, 2.9, 3.5 and 2.2 for ≥ 2 readmissions, respectively (Table 3).

Discussion

This study found early emergency readmission frequency was a significant predictor of all-cause mortality within 30 days or 6 months post hospital discharge, and also over a 2-year period, in all age groups spanning 18–107 years, independent of presenting diagnosis, LOS, age and sex.

Table 2 Health conditions presented as primary diagnoses and prolonged length of stay during first hospital admission in relation to subsequent readmission frequency

LOS length of stay

a Specific conditions within categories of disorders

Primary diagnosis on first admission Disorders from first admission in relation to

subsequent readmission frequency (%) χ2 p No readmission 1 readmission ≥ 2 read-

missions

Sepsis 3.4 5.1 6.5 47.1 < 0.001

Viral infections 0.5 0.3 0.2 3.3 0.194

Haematological disorders 2.2 1.1 1.0 20.3 < 0.001

Metabolic and endocrine disorders 3.3 4.0 4.3 6.5 0.039

Diabetes 1.0 1.6 2.2 20.8 < 0.001

Psychiatric disorders 0.8 1.2 2.1 23.8 < 0.001

Neurological disorders 2.3 1.8 2.7 3.2 0.198

Ophthalmic disorders 0.5 0.2 0.3 8.7 0.013

Cardiovascular disorders 13.7 13.3 13.0 0.9 0.630

Congestive heart failurea 1.1 2.6 3.2 80.1 < 0.001

Pulmonary disorders 9.4 16.8 21.3 279.3 < 0.001

Asthmaa 0.7 0.5 0.5 1.6 0.439

Chronic obstructive pulmonary diseasea 1.3 2.3 4.8 106.2 < 0.001

Pneumoniaa 4.6 9.7 10.8 193.1 < 0.001

Gastrointestinal disorders 12.9 12.5 11.1 3.6 0.169

Dermatological disorders 3.4 2.9 3.4 1.7 0.436

Musculoskeletal disorders 10.5 5.1 2.7 144.4 < 0.001

Urological disorders 11.3 12.3 12.1 2.8 0.237

Urinary tract infectiona 3.1 5.7 6.9 86.1 < 0.001

Bodily pain 13.8 11.9 8.4 31.0 < 0.001

Bone fractures 8.2 7.7 6.6 4.1 0.128

Hip fracturesa 1.9 2.7 2.0 7.0 0.030

Medical device-related complications 3.1 3.7 4.3 8.0 0.019

Prolonged LOS in hospital (≥ 2 weeks) 7.8 21.6 39.3 1557 < 0.001

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Our findings are highly relevant both to clinical research and healthcare planning to further examine underlying rea- sons for the rising rates of early readmissions in England [5, 6].

We observed that short-term mortality rates (within 30 days of discharge) were similar for patients who were readmitted either once or for ≥ 2 times. However, mortal- ity rates in those who were readmitted ≥ 2 times rose to a higher level when longer-term (within 6 months or over a 2-year period) mortality rates were examined. These dif- ferences could be explained, in part, by the higher preva- lence of underlying conditions presented on first admission among those who were readmitted multiple times but who were able to survive over a short period, i.e. proportion- ally more of those readmitted ≥ 2 times die after 30 days of discharge than those who were readmitted once. Another explanation of this result could be that the 30-day mortality is an endpoint observed too early for patients readmitted ≥ 2 times, as these patients may have lived long enough to be admitted to the hospital two times or more. However, we did not have the timings between first hospital discharge and second readmission in this group of patients. It is likely that there are other reasons yet to be elucidated since readmis- sion frequency predicts mortality independently of condi- tions presented on admission. This fundamental conundrum has been highlighted by Healthwatch England because the rates of readmissions continue to rise steadily in England without a clear explanation [5]. There are likely to be many factors contributing to this rise, including aspects related to patient discharge activities, such as the lack of explanation of the discharge plan provided to the patient, poor execution of discharge instructions, and lack of communication with primary care and coordination of care post-discharge, all of which are risk factors for avoidable readmissions [16].

Efforts have been made to reduce hospital readmissions, such as the Hospital Readmissions Reduction Program in the US, with mixed results [17]. A recent study showed that telephone contact with patients within 48 h of discharge lowered early readmission rates compared with those who had no contact (9.2% versus 15.7%, p = 0.011) [18]. Further research is therefore needed, focussing on safe discharge [19] and follow-up care for patients [20–22] and effective communication with primary care physicians [23]. The uncoupling of mortality rates between frequency of readmis- sions narrowed in the oldest age group may be due to higher rates of mortality or palliation in the community among this group which prevent them from readmissions.

Fig. 1 Stacked bar charts showing rates of mortality within 30 days (a) and 6 months (b) of discharge from first admission, and over a 2-year period (c) according to readmission status in different age cat- egories. Note: the numbers represent the rates of mortality within each category of readmission status

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Readmission frequency is routinely documented and assessed by secondary care centres as a way to identify fre- quent hospital users to provide care-support for patients in the community, [2] which both benefits patients and health- care services. Evidence from this study further extends its utility as a simple and practical indicator of post-discharge mortality, even just a single readmission within 28 days of hospital discharge can identify those at high risk of death.

The overall rates of readmission of 11.6% (8.3% once and 3.3% ≥ 2 times) observed in our study are at the lower end of those reported previously (11.6–18.4%) [24–26]. One factor is likely to be due to age differences between various study populations. As observed in our study of the incremental increase in readmission rates with age. The mortality rates observed in our study were also comparable to those recently reported for 30 days [27] and 6 months of discharge [28, 29]. As expected, the rates of mortality also increased with age. However, within each age category, the rates of mortal- ity were consistently higher among individuals who were readmitted to hospital more frequently, suggesting that the risk of death from readmissions extends to the less-studied younger adults. Our recent study found that a risk of pro- longed LOS in hospital (> 17 days) for patients admitted with hip fractures was increased by fourfold in those who acquired nosocomial pneumonia or a urinary tract infec- tion [30]. This raises the possibility that patients who face emergency readmissions spend overall a much longer time in the risky hospital environment, such that mortality could, at least in part, be linked to hospital acquired complications.

Conditions presented on admission may also play a key role in future readmissions and mortality. In the UK, the rates of admission with non-specific chest pain (22%) and abdomi- nal pains (20%), urinary tract infection (11%), acute mental crisis (10%), COPD (10%) and angina (6%) are among the highest in patients > 75 years [31]. Excess admission rates have been reported to occur for pneumonia, congestive heart failure and COPD in older individuals and during the months of the influenza season [32].

The strengths of this study lie in its large number of consecutive patients, which enable us to estimate the risk of mortality by decades of age, ranging from 18 to 107 years. Appropriate adjustments were made including age, sex and presenting diagnoses. Characteristics of this study have been shown to be similar to those of the UK population [12, 33, 34]. There are certain limitations in the present study that may arise from patients who have left our catchment area or those who were readmitted to other centres this would lead to an underestimation of

Fig. 2 Mortality rates within 30  days (a) and 6 months (b) of dis- charge from first admission, and over a 2-year period (c) according to admission frequency in different age categories. Denotes differences from “no readmission group”: p < 0.001

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readmission rates. Studies have variably used between 28 and 30 days to define the period of early emergency readmission, [35] while we used 28 days for our defini- tion, as guided by the NHS, [36] which would capture slightly lower numbers of readmissions. However, these differences do not affect the outcome of our studies since the primary purpose of our study was not intended for

comparison of the performance of the frequency of read- missions with other studies.

In conclusion, early hospital readmission predicts short-, medium- and long-term mortality post-discharge from hospital in adults aged 18–107 years, independent of underlying presenting conditions, LOS, age and sex.

Further research focussing on safe discharge and follow- up patient care may help reduce preventable or avoidable readmissions and post-discharge mortality.

Acknowledgements The authors wish to thank patients and all those who were involved in the project, and Dr Erica Heppleston (Quality Department, Ashford and St Peter’s Hospitals NHS Foundation Trust) who performed the study coordination and data collection.

Author contributions TSH and DF reviewed the topic related literature and performed the study concept and analysis design. DF, PM and JR Table 3 Stepwise multivariable logistic and Cox regression examining the association of readmission frequency and mortality, adjusted for age, sex and conditions presented as primary diagnosis (reported in Table 1) and prolonged LOS

a Age and sex adjustment for all analyses including individual age bands

b Reference group: No readmission; OR odds ratio; HR hazard ratio Logistic regression Age and sex adjusted mortalitya

1 readmissionb ≥ 2 readmissionsb

Event rate (%) OR 95% CI p Event rate (%) OR 95% CI p

Death within 30 days

 All ages 9.2 3.4 2.9–4.0 < 0.001 10.0 2.6 2.0–3.3 < 0.001

 18–49 years 1.2 10.7 3.6–32.0 < 0.001 1.1 10.2 1.3–81.4 0.028

 50–59 years 2.4 3.9 1.5–10.0 0.006 4.6 5.6 1.5–20.9 0.010

 60–69 years 6.3 4.0 2.3–6.9 < 0.001 7.1 3.7 1.6–8.2 0.002

 70–79 years 8.9 3.2 2.2–4.7 < 0.001 9.9 2.8 1.7–4.8 < 0.001

  ≥ 80 years 14.2 3.2 2.6–4.0 < 0.001 12.6 2.4 1.8–3.1 < 0.001

Death within 6 months

 All ages 19.6 3.0 2.7–3.4 < 0.001 27.4 3.4 2.9–4.0 < 0.001

  18–49 years 2.6 7.5 3.4–15.4 < 0.001 5.7 12.4 4.3–35.5 < 0.001

  50–59 years 7.6 4.5 2.6–7.8 < 0.001 10.8 5.9 2.5–14.2 < 0.001

  60–69 years 13.9 3.3 2.3–4.8 < 0.001 22.3 4.1 2.5–6.9 < 0.001

  70–79 years 19.8 3.2 2.5–4.2 < 0.001 32.0 5.1 3.6–7.1 < 0.001

   ≥ 80 years 29.5 2.7 2.2–3.2 < 0.001 31.7 2.7 2.2–3.3 < 0.001

Cox regression Age and sex adjusted mortalitya

1 readmissionb ≥ 2 readmissionsb

Event rate (%) HR 95% CI p Event rate (%) HR 95% CI p

Death over two years

 All ages 25.5 2.2 2.0–2.4 < 0.001 36.1 2.5 2.2–2.8 < 0.001

 18–49 years 3.1 4.9 2.6–9.0 < 0.001 10.3 12.4 5.8–26.6 < 0.001

 50–59 years 10.0 3.3 2.1–5.1 < 0.001 13.8 3.7 1.8–7.5 < 0.001

 60–69 years 18.0 2.7 2.0–3.6 < 0.001 25.0 2.9 1.9–4.4 < 0.001

 70–79 years 28.0 2.5 2.0–3.0 < 0.001 41.9 3.5 2.7–4.4 < 0.001

≥ 80 years 37.6 2.0 1.8–2.3 < 0.001 42.4 2.2 1.9–2.5 < 0.001

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commented on the manuscript. TSH wrote the first draft, analysed, interpreted the data and revised the manuscript. CHF edited the manu- script. All authors checked, interpreted results and approved the final version.

Funding None.

Compliance with ethical standards

Conflict of interest The authors declare that they have no conflicts of interest.

Ethical approval This study does not require NHS Research Ethics Committee approval since it involves secondary analysis of anonymised data. This study was conducted in accordance with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Human and animal rights This article does not contain any studies with animals performed by any of the authors.

Informed consent Informed consent was obtained from all individual participants included in the study.

Open Access This article is licensed under a Creative Commons Attri- bution 4.0 International License, which permits use, sharing, adapta- tion, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not

permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0/.

References

1. Benbassat J, Taragin M (2000) Hospital readmissions as a measure of quality of health care, advantages and limitations. Arch Intern Med 160:1074–1081. https ://doi.org/10.1001/archi nte.160.8.1074 2. Nolte E, Roland M, Guthrie S, Brereton L (2012) Preventing

emergency readmissions to hospital: a scoping review. Rand Health Q 2:10

3. Mayr FB, Talisa VB, Balakumar V, Chang CC, Fine M, Yende S (2017) Proportion and cost of unplanned 30-day readmissions after sepsis compared with other medical conditions. JAMA 317:530–531. https ://doi.org/10.1001/jama.2016.20468

4. Shah RM, Zhang Q, Chatterjee S, Cheema F, Loor G, Lemaire SA et al (2019) Incidence, cost, and risk factors for readmission after coronary artery bypass grafting. Ann Thorac Surg 107:1782–

1789. https ://doi.org/10.1016/j.athor acsur .2018.10.077

5. Healthwatch (2018) Emergency readmissions: what’s changed one year on? https ://www.healt hwatc h.co.uk/repor t/2018-11-14/

emerg ency-readm issio ns-whats -chang ed-one-year. Accessed 15 Nov 2020

6. Healthwatch (2019) New plans to investigate rising emergency readmissions to hospitals announced in response to concerns raised by patients. https ://www.healt hwatc h.co.uk/news/2019- 01-17/new-plans -inves tigat e-risin g-emerg ency-readm isson s-hospi tals-annou nced-respo nse. Accessed 15 Nov 2020

Fig. 3 Kaplan–Meier survival plot in patients with different frequency of readmissions. The table beneath the figure shows the number of at-risk patients at various time points for the three readmission frequency cohorts

(9)

7. Friebel R (2020) Trends in the number of English NHS hospital admissions, 2006 to 2016. The Health Foundation. https ://www.

healt h.org.uk/chart /chart -trend s-in-the-numbe r-of-engli sh-nhs- hospi tal-admis sions -2006-to-2016. Accessed 15 Nov 2020 8. Karakusevic S (2016) Understanding patient flow in hospitals.

Nuffield Trust, London

9. Mudireddy P, Scott F, Feathers A, Lichtenstein GR (2017) Inflam- matory bowel disease: predictors and causes of early and late hos- pital readmissions. Inflamm Bowel Dis 23:1832–1839. https ://doi.

org/10.1097/MIB.00000 00000 00124 2

10. Agrawal S, Garg L, Shah M, Agarwal M, Patel B, Singh A et al (2018) Thirty-day readmissions after left ventricular assist device implantation in the United States: insights from the Nationwide Readmissions Database. Circ Heart Fail 11:e004628. https ://doi.

org/10.1161/CIRCH EARTF AILUR E.117.00462 8

11. Lewis KL, Fanaian M, Kotze B, Grenyer BF (2019) Mental health presentations to acute psychiatric services: 3-year study of preva- lence and readmission risk for personality disorders compared with psychotic, affective, substance or other disorders. BJPsych Open 5:e1. https ://doi.org/10.1192/bjo.2018.72

12. Fry CH, Heppleston E, Fluck D, Han TS (2020) Derivation of age-adjusted LACE index thresholds in the prediction of mortality and frequent hospital readmissions in adults. Intern Emerg Med 28:1–7. https ://doi.org/10.1007/s1173 9-020-02448 -3

13. Heppleston E, Fry CH, Kelly K, Shepherd B, Wright R, Jones G et al (2020) LACE index predicts age-specific unplanned readmis- sions and mortality after hospital discharge. Aging Clin Exp Res.

https ://doi.org/10.1007/s4052 0-020-01609 -w

14. World Health Organization (2004) ICD-10: international statisti- cal classification of diseases and related health problems: tenth revision. WHO, Geneva

15. https ://digit al.nhs.uk/data-and-infor matio n/publi catio ns/clini cal- indic ators /nhs-outco mes-frame work/archi ve/nhs-outco mes-frame work-indic ators ---febru ary-2019-relea se. Accessed 15 Nov 2020 16. Aspenson M, Hazary S (2012) The clock is ticking on readmission

penalties. Healthc Financ Manag 66(7):58–63

17. Wadhera RK, Maddox KE, Wasfy JH, Haneuse S, Shen C, Yeh RW (2018) Association of the Hospital Readmissions Reduction Program with mortality among Medicare beneficiaries hospital- ized for heart failure, acute myocardial infarction, and pneumonia.

JAMA 320:2542–2552. https ://doi.org/10.1001/jama.2018.19232 18. Vernon D, Brown JE, Griffiths E, Nevill AM, Pinkney M (2019)

Reducing readmission rates through a discharge follow-up service.

Future Healthc J 6:114–117. https ://doi.org/10.7861/futur ehosp .6-2-114

19. Wong SP, Sharda N, Zietlow KE, Heflin MT (2020) Planning for a safe discharge: more than a capacity evaluation. J Am Geriatr Soc 68:859–866. https ://doi.org/10.1111/jgs.16315

20. Agostinho JR, Gonçalves I, Rigueira J, Aguiar-Ricardo I, Nunes- Ferreira A, Santos R et al (2019) Protocol-based follow-up pro- gram for heart failure patients: Impact on prognosis and quality of life. Rev Port Cardiol 38:755–764. https ://doi.org/10.1016/j.

repc.2019.03.006

21. Catanach B, Betz ME, Tvrdy C, Skelding C, Brummett S, Allen MH (2019) Implementing an emergency department telephone follow-up program for suicidal patients, successes and challenges.

Jt Comm J Qual Patient Saf 45:725–732. https ://doi.org/10.1016/j.

jcjq.2019.07.009

22. de Mestral C, Kayssi A, Al-Omran M, Salata K, Hussain MA, Roche-Nagle G (2019) Home care nursing after elective vascular surgery, an opportunity to reduce emergency department visits and hospital readmission. BMJ Qual Saf 28:901–907. https ://doi.

org/10.1136/bmjqs -2018-00916 1

23. Destino LA, Dixit A, Pantaleoni JL, Wood MS, Pageler NM, Kim J et al (2017) Improving communication with primary care

physicians at the time of hospital discharge. Jt Comm J Qual Patient Saf 43:80–88. https ://doi.org/10.1016/j.jcjq.2016.11.005 24. Gruneir A, Dhalla IA, van Walraven C, Fischer HD, Camacho

X, Rochon PA et al (2011) Unplanned readmissions after hospi- tal discharge among patients identified as being at high risk for readmission using a validated predictive algorithm. Open Med 5:e104-111

25. Lim E, Matthew N, Mok W, Chowdhury S, Lee D (2011) Using hospital readmission rates to track the quality of care in public hospitals in Singapore. BMC Health Serv Res 11(Suppl 1):A16.

https ://doi.org/10.1186/1472-6963-11-S1-A16

26. Tan SY, Low LL, Yang Y, Lee KH (2013) Applicability of a previ- ously validated readmission predictive index in medical patients in Singapore, a retrospective study. BMC Health Serv Res 13:366.

https ://doi.org/10.1186/1472-6963-13-366

27. Dharmarajan K, Wang Y, Lin Z, Normand SL, Ross JS, Horwitz LI et al (2017) Association of changing hospital readmission rates with mortality rates after hospital discharge. JAMA 318:270–278.

https ://doi.org/10.1001/jama.2017.8444

28. Mazzola P, Bellelli G, Broggini V, Anzuini A, Corsi M, Berruti D et al (2015) Postoperative delirium and pre-fracture disabil- ity predict 6-month mortality among the oldest old hip fracture patients. Aging Clin Exp Res 27:53–60. https ://doi.org/10.1007/

s4052 0-014-0242-y

29. Park LP, Chu VH, Peterson G, Skoutelis A, Lejko-Zupa T, Bouza E et al (2016) Validated risk score for predicting 6-month mortal- ity in infective endocarditis. J Am Heart Assoc 5:e003016. https ://doi.org/10.1161/JAHA.115.00301 6

30. Lisk R, Uddin M, Parbhoo A, Yeong K, Fluck D, Sharma P, Lean ME, Han TS (2019) Predictive model of length of stay in hospital among older patients. Aging Clin Exp Res 31:993–999. https ://

doi.org/10.1007/s4052 0-018-1033-7

31. O’Cathain A, Knowles E, Maheswaran R, Pearson T, Turner J, Hirst E, Goodacre S, Nicholl J (2014) A system-wide approach to explaining variation in potentially avoidable emergency admis- sions: national ecological study. BMJ Qual Saf 23:47–55. https ://

doi.org/10.1136/bmjqs -2013-00200 3

32. Yap FH, Ho PL, Lam KF, Chan PK, Cheng YH, Peiris JS (2004) Excess hospital admissions for pneumonia, chronic obstructive pulmonary disease, and heart failure during influenza seasons in Hong Kong. J Med Virol 73:617–623. https ://doi.org/10.1002/

jmv.20135

33. Han TS, Fry CH, Fluck D, Affley B, Gulli G, Barrett C et al (2017) Evaluation of anticoagulation status for atrial fibrillation on early ischaemic stroke outcomes, a registry-based, prospective cohort study of acute stroke care in Surrey, UK. BMJ Open 7:e019122.

https ://doi.org/10.1136/bmjop en-2017-01912 2

34. Han TS, Fry CH, Gulli G, Affley B, Robin J, Irvin-Sellers M, Fluck D et al (2020) Prestroke disability predicts adverse post- stroke outcome: a registry-based prospective cohort study of acute stroke. Stroke 51:594–600. https ://doi.org/10.1161/STROK EAHA.119.02774 0

35. Zhou H, Della PR, Roberts P, Goh L, Dhaliwal SS (2016) Utility of models to predict 28-day or 30-day unplanned hospital readmis- sions: an updated systematic review. BMJ Open 6:6

36. NHS Digital (2010) Archived Emergency readmissions to hospital within 28 days of discharge, financial year 2010/11. https ://files .digit al.nhs.uk/BB/42FA8 5/hes-emer-read-hosp-28-days-disc- 2001-2011-pra.pdf. Accessed 15 Nov 2020

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